ARCHIVES

1. Y. Liu, P. Sun, N. Wergeles, and Y. Shang, “A survey and performance evaluation of deep learning methods for small object detection,”
Expert Syst Appl, vol. 172, Jun. 2021, doi: 10.1016/j.eswa.2021.114602.
2. Z. Cai and N. Vasconcelos, “Cascade R-CNN: High quality object detection and instance segmentation,” IEEE Trans Pattern Anal Mach
Intell, vol. 43, no. 5, pp. 1483–1498, May 2021, doi: 10.1109/TPAMI.2019.2956516.
3. D. J. Yeong, G. Velasco-hernandez, J. Barry, and J. Walsh, “Sensor and sensor fusion technology in autonomous vehicles: A review,”
Sensors, vol. 21, no. 6. MDPI AG, pp. 1–37, Mar. 02, 2021. doi: 10.3390/s21062140.
4. D. Feng et al., “Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and
Challenges,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 3, pp. 1341–1360, Mar. 2021, doi:
10.1109/TITS.2020.2972974.
5. X. Sun, P. Wang, C. Wang, Y. Liu, and K. Fu, “PBNet: Part-based convolutional neural network for complex composite object detection in
remote sensing imagery,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 173, pp. 50–65, Mar. 2021, doi:
10.1016/j.isprsjprs.2020.12.015.
6. S. Kuutti, R. Bowden, Y. Jin, P. Barber, and S. Fallah, “A Survey of Deep Learning Applications to Autonomous Vehicle Control,” IEEE
Transactions on Intelligent Transportation Systems, vol. 22, no. 2, pp. 712–733, Feb. 2021, doi: 10.1109/TITS.2019.2962338.
7. Y. Cai et al., “YOLOv4-5D: An Effective and Efficient Object Detector for Autonomous Driving,” IEEE Trans Instrum Meas, vol. 70, 2021,
doi: 10.1109/TIM.2021.3065438.
8. S. Sun, N. Akhtar, H. Song, A. Mian, and M. Shah, “Deep Affinity Network for Multiple Object Tracking,” IEEE Trans Pattern Anal Mach
Intell, vol. 43, no. 1, pp. 104–119, Jan. 2021, doi: 10.1109/TPAMI.2019.2929520.
9. Z. Huang, J. Wang, X. Fu, T. Yu, Y. Guo, and R. Wang, “DC-SPP-YOLO: Dense connection and spatial pyramid pooling based YOLO for
object detection,” Inf Sci (N Y), vol. 522, pp. 241–258, Jun. 2020, doi: 10.1016/j.ins.2020.02.067.
10. X. Zhao, P. Sun, Z. Xu, H. Min, and H. Yu, “Fusion of 3D LIDAR and Camera Data for Object Detection in Autonomous Vehicle
Applications,” IEEE Sens J, vol. 20, no. 9, pp. 4901–4913, May 2020, doi: 10.1109/JSEN.2020.2966034.
11. K. Tong, Y. Wu, and F. Zhou, “Recent advances in small object detection based on deep learning: A review,” Image and Vision Computing,
vol. 97. Elsevier Ltd, May 01, 2020. doi: 10.1016/j.imavis.2020.103910.
12. B. Mishra, D. Garg, P. Narang, and V. Mishra, “Drone-surveillance for search and rescue in natural disaster,” Comput Commun, vol. 156,
pp. 1–10, Apr. 2020, doi: 10.1016/j.comcom.2020.03.01213. L. Wen et al., “UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking,” Computer Vision and Image
Understanding, vol. 193, Apr. 2020, doi: 10.1016/j.cviu.2020.102907.
14. L. Zhao and S. Li, “Object detection algorithm based on improved YOLOv3,” Electronics (Switzerland), vol. 9, no. 3, Mar. 2020, doi:
10.3390/electronics9030537.
15. M. Elhoseny, “Multi-object Detection and Tracking (MODT) Machine Learning Model for Real-Time Video Surveillance Systems,” Circuits
Syst Signal Process, vol. 39, no. 2, pp. 611–630, Feb. 2020, doi: 10.1007/s00034-019-01234-7.
16. M. Kristo, M. Ivasic-Kos, and M. Pobar, “Thermal Object Detection in Difficult Weather Conditions Using YOLO,” IEEE Access, vol. 8,
pp. 125459–125476, 2020, doi: 10.1109/ACCESS.2020.3007481.
17. W. Fang, L. Wang, and P. Ren, “Tinier-YOLO: A Real-Time Object Detection Method for Constrained Environments,” IEEE Access, vol.
8, pp. 1935–1944, 2020, doi: 10.1109/ACCESS.2019.2961959.
18. Y. Pi, N. D. Nath, and A. H. Behzadan, “Convolutional neural networks for object detection in aerial imagery for disaster response and
recovery,” Advanced Engineering Informatics, vol. 43, Jan. 2020, doi: 10.1016/j.aei.2019.101009.
19. H. Song, H. Liang, H. Li, Z. Dai, and X. Yun, “Vision-based vehicle detection and counting system using deep learning in highway scenes,”
European Transport Research Review, vol. 11, no. 1, Dec. 2019, doi: 10.1186/s12544-019-0390-4.
20. Z. Q. Zhao, P. Zheng, S. T. Xu, and X. Wu, “Object Detection with Deep Learning: A Review,” IEEE Transactions on Neural Networks and
Learning Systems, vol. 30, no. 11. Institute of Electrical and Electronics Engineers Inc., pp. 3212–3232, Nov. 01, 2019. doi:
10.1109/TNNLS.2018.2876865.
21. E. Arnold, O. Y. Al-Jarrah, M. Dianati, S. Fallah, D. Oxtoby, and A. Mouzakitis, “A Survey on 3D Object Detection Methods for Autonomous
Driving Applications,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 10, pp. 3782–3795, Oct. 2019, doi:
10.1109/TITS.2019.2892405.
22. X. Wu, D. Hong, J. Tian, J. Chanussot, W. Li, and R. Tao, “ORSIm Detector: A Novel Object Detection Framework in Optical Remote
Sensing Imagery Using Spatial-Frequency Channel Features,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 7, pp.
5146–5158, Jul. 2019, doi: 10.1109/TGRS.2019.2897139.
23. A. Borji, M. M. Cheng, Q. Hou, H. Jiang, and J. Li, “Salient object detection: A survey,” Computational Visual Media, vol. 5, no. 2.
Tsinghua University Press, pp. 117–150, Jun. 01, 2019. doi: 10.1007/s41095-019-0149-9.
24. X. Hu et al., “SINet: A Scale-Insensitive Convolutional Neural Network for Fast Vehicle Detection,” IEEE Transactions on Intelligent
Transportation Systems, vol. 20, no. 3, pp. 1010–1019, Mar. 2019, doi: 10.1109/TITS.2018.2838132.
25. C. Cao et al., “An Improved Faster R-CNN for Small Object Detection,” IEEE Access, vol. 7, pp. 106838–106846, 2019, doi:
10.1109/ACCESS.2019.2932731.